Serum pharmacodynamic biomarkers for chronic corticosteroid treatment of children
Bibliographic record
Abstract
Corticosteroids are extensively used in pediatrics, yet the burden of side effects is significant. Availability of a simple, fast, and reliable biochemical read out of steroidal drug pharmacodynamics could enable a rapid and objective assessment of safety and efficacy of corticosteroids and aid development of corticosteroid replacement drugs. To identify potential corticosteroid responsive biomarkers we performed proteome profiling of serum samples from DMD and IBD patients with and without corticosteroid treatment using SOMAscan aptamer panel testing 1,129 proteins in <0.1 cc of sera. Ten pro-inflammatory proteins were elevated in untreated patients and suppressed by corticosteroids (MMP12, IL22RA2, CCL22, IGFBP2, FCER2, LY9, ITGa1/b1, LTa1/b2, ANGPT2 and FGG). These are candidate biomarkers for anti-inflammatory efficacy of corticosteroids. Known safety concerns were validated, including elevated non-fasting insulin (insulin resistance), and elevated angiotensinogen (salt retention). These were extended by new candidates for metabolism disturbances (leptin, afamin), stunting of growth (growth hormone binding protein), and connective tissue remodeling (MMP3). Significant suppression of multiple adrenal steroid hormones was also seen in treated children (reductions of 17-hydroxyprogesterone, corticosterone, 11-deoxycortisol and testosterone). A panel of new pharmacodynamic biomarkers for corticosteroids in children was defined. Future studies will need to bridge specific biomarkers to mechanism of drug action, and specific clinical outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".